As demands of medical care and technological development, the electronic, information and communication technologies are widely applied to medical care. Take caring of infants or patients as an example. The monitoring of cardio activity is an important part. In infants and young children, for example, to monitor the heart rate and respiratory activity can significantly prevent the occurrence of heart activity disorders in infants, which often resulting in the subsequent chain of health problems and even sudden death crisis. Therefore, baby monitors used in monitoring and caring is common. Also, the medical or physiological monitoring equipments which are commonly used in the clinical care of bedridden patients are mostly contact-based monitoring devices to capture breathing and heartbeat signals of the patient, such as, using detection electrodes attached to the chest skin and using monitors, heart rate band, or finger-type detection equipment. However, for contact-based detection, a disadvantage is the possible discomfort on the skin. In addition, the clinical monitoring equipments are expensive and do not meet the demand for home care use. There are other care products designed for the detection of human respiration and heartbeat caused by slight vibration. The principle is based on a mattress or lying sensing element mounted to detect the slight vibrations transmitted to a measuring and monitoring apparatus. These technologies still have a low resolution problem, and are vulnerable to environmental vibration noise interference.
Massachusetts Institute of Technology (MIT) disclosed a method of detecting human heartbeat using dynamic images (pulse). This approach, after face detection, uses image-processing algorithm to detect the changes in skin color features invisible to naked eyes in continuous image of a human face, for example, RGB values in the face image, amplify the signal, filter out the noise, and use Blind Source Separation analysis to distinguish slight changes in facial blood flow under various states of the human heart rates, and then estimates a heart rate. As such, a non-contact detection can be obtained. Similarly, Gdańsk University of Technology, Poland research team has also made a similar image computing technologies to detect a heartbeat.
In practical application, the current image-based technology for detecting heart rate activity usually has the following disadvantages. First, the existing image detection technology demands the monitored objects to be in certain posture or stability, for example, a front continuous stationary posture. In particular, the existing technology typically uses human face as the image acquisition target. However, the infant's actions and behavior are usually difficult to control and meet the basic requirements of face recognition; therefore, the image is difficult for use in this type of detection technology relying on face recognition. Furthermore, infant cardiopulmonary function, physiological state and behavior are very different from the adults. To adopt cardiopulmonary imaging techniques to monitor the physiological status of infants and young children, there are still many technical barriers to be overcome. Finally, in order to achieve accurate detection of the target, the use of face recognition technology sensing devices (i.e., cameras) requires relatively higher sensitivity and resolution, thereby increasing the cost for equipment purchases.
In response to these problems, Rutgers University research team (Elgammal) proposed to replace face detection with the human skin color detection technology. However, the existing techniques for human skin color detection have an error rate as high as 15-30%, and are easy to mistake non-human object with color similar to real skin color for human, resulting in false alarm, interference calculation and interpretation. Furthermore, the known skin color detection techniques treat disconnected regions of pixels as different monitored targets, and can not determine the relevance between disconnected skin color regions, or the relevance between each skin color region and the monitored target, which often results in gaps between information processing and interpretation.
In summary, the known image-based monitoring techniques still have many problems to overcome.